This dataset contains aerial images and digital surface models of 19 out of 20 hydrological subcatchments analysed in the project "Shortcut". The subcatchments mostly lie in rural areas of the Swiss midlands and the datasets were obtained by flights with an eBee drone (SenseFly) between October 2017 and August 2018. The resolutions of the aerial images and the digital surface models lie between 2cm/pixel and 5cm/pixel, depending on the catchment. The aerial images in this dataset were used for the publication "Hydraulic Shortcuts Increase the Connectivity of Arable Land Areas to Surface Waters" (Schönenberger, U. & Stamm C, 2021)". The publication, supporting information, datasets, and codes can be found on the Eawag Research Data Institutional Repository (DOI: 10.25678/0003J3).

What does the data show? A Growing Degree Day (GDD) is a day in which the average temperature is above 5.5°C. It is the number of degrees above this threshold that counts as a Growing Degree Day. For example if the average temperature for a specific day is 6°C, this would contribute 0.5 Growing Degree Days to the annual sum, alternatively an average temperature of 10.5°C would contribute 5 Growing Degree Days. Given the data shows the annual sum of Growing Degree Days, this value can be above 365 in some parts of the UK.Annual Growing Degree Days are calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of GDD to previous values. What are the possible societal impacts?Annual Growing Degree Days indicate if conditions are suitable for plant growth. An increase in GDD can indicate larger crop yields due to increased crop growth from warm temperatures, but crop growth also depends on other factors. For example, GDD do not include any measure of rainfall/drought, sunlight, day length or wind, species vulnerability, or plant dieback in extremely high temperatures. GDD can indicate increased crop growth until temperatures reach a critical level above which there are detrimental impacts on plant physiology.GDD does not estimate the growth of specific species and is not a measure of season length.What is a global warming level?Annual Growing Degree Days are calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming.   The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Growing Degree Days, an average is taken across the 21 year period. Therefore, the Annual Growing Degree Days show the number of growing degree days that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each global warming level and two baselines. They are named 'GDD' (Growing Degree Days), the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘GDD 2.5 median’ is the median value for the 2.5°C projection. Decimal points are included in field aliases but not field names e.g. ‘GDD 2.5 median’ is ‘GDD_25_median’. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘GDD 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, Annual Growing Degree Days were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past.  Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

Surface runoff represents a major pathway for pesticide transport from agricultural areas to surface waters. The influence of man-made structures (e.g. roads, hedges, ditches) on surface runoff connectivity has been shown in various studies. In Switzerland, so-called hydraulic shortcuts (e.g. inlets and maintenance manholes of road or field storm drainage systems) have been shown to influence surface runoff connectivity and related pesticide transport. Their occurrence, and their influence on surface runoff and pesticide connectivity have however not been studied systematically. To address that deficit, we randomly selected 20 study areas (average size = 3.5 km2) throughout the Swiss plateau, representing arable cropping systems. We assessed shortcut occurrence in these study areas using three mapping methods: field mapping, drainage plans, and high-resolution aerial images. Surface runoff connectivity in the study areas was analysed using a 2x2 m digital elevation model and a multiple-flow algorithm. Parameter uncertainty affecting this analysis was addressed by a Monte Carlo simulation. With our approach, agricultural areas were divided into areas that are either directly connected to surface waters, indirectly (i.e. via hydraulic shortcuts), or not connected at all. Finally, the results of this connectivity analysis were scaled up to the national level using a regression model based on topographic descriptors and were then compared to an existing national connectivity model. Inlets of the road storm drainage system were identified as the main shortcuts. On average, we found 0.84 inlets and a total of 2.0 manholes per hectare of agricultural land. In the study catchments between 43 and 74 % of the agricultural area is connected to surface waters via hydraulic shortcuts. On the national level, this fraction is similar and lies between 47 and 60 %. Considering our empirical observations led to shifts in estimated fractions of connected areas compared to the previous connectivity model. The differences were most pronounced in flat areas of river valleys. These numbers suggest that transport through hydraulic shortcuts is an important pesticide flow path in a landscape where many engineered structures exist to drain excess water from fields and roads. However, this transport process is currently not considered in Swiss pesticide legislation and authorisation. Therefore, current regulations may fall short to address the full extent of the pesticide problem. However, independent measurements of water flow and pesticide transport to quantify the contribution of shortcuts and validating the model results are lacking. Overall, the findings highlight the relevance of better understanding the connectivity between fields and receiving waters and the underlying factors and physical structures in the landscape.

Quarterly Sampling Reports for antimicrobial resistance, specifically in cecal and product. Cecal reports consists of data for Salmonella, Campylobacter, E.coli, and Enterococcus. Product reports consists of data for Salmonella, Campylobacter, and Escherichia coli O157:H7 and non-O157 Shiga toxin-producing Escherichia coli (STEC). All reports are updated quarterly. See the FSIS website for additional information.

Pulse-field gel electrophoresis (PFGE) Campylobacter reports for FSIS Raw Products from fiscal year (FY) 2016 to FY2019. See FSIS website for additional information.

Establishment specific sampling results for Pasteurized Egg Products. Current data is updated quarterly; archive data is updated annually. See the FSIS website for additional information.

These data provide additional demographic information about FSIS regulated establishments. Additional demographic data are also available in the FSIS Meat, Poultry, and Egg Inspection Directory (MPI). The Meat, Poultry and Egg Product Inspection Directory is a listing of establishments that produce meat, poultry, and/or egg products regulated by USDA's Food Safety and Inspection Service (FSIS).

FSIS’ FoodKeeper application educates users about food and beverages storage to help them maximize the freshness and quality of these items. By helping users understand food storage, the application empowers consumers to choose storage methods that extend the shelf life of their items. By doing so users will be able to keep items fresh longer than if they were not stored properly.

The Meat, Poultry and Egg Product Inspection Directory is a listing of establishments that produce meat, poultry, and/or egg products regulated by USDA's Food Safety and Inspection Service (FSIS) pursuant to the Federal Meat Inspection Act, the Poultry Products Inspection Act, and the Egg Products Inspection Act. The directory is updated weekly, and the current edition replaces all previous editions.

Pulse-field gel electrophoresis (PFGE) E.Coli data for FSIS Raw Beef Products from fiscal year (FY) 2016 to FY2019. See the FSIS website for additional information.

Quarterly sampling report for FSIS Raw Beef Products. The report will provide data on the percent positive of E.Coli (O157:H7 and non-O157 Shiga-toxin producing) in FSIS Raw Beef Products. See the FSIS website for additional information.

Establishment specific sampling results for FSIS Ready-to-Eat (RTE) sampling projects. Current data is updated quarterly; archive data is updated annually. See the FSIS website for additional information.

Pulse-field gel electrophoresis (PFGE) Salmonella reports for FSIS Raw Products from fiscal year (FY) 2016 to FY2019. See FSIS website for additional information.

Percent positive and serotype quarterly sampling information for Salmonella and Campylobacter in FSIS Raw Products. Reports are updated quarterly. See FSIS website for additional information.

This datasets summarizes and lists all the recalls of meat and poultry products produced by FSIS federally inspected establishments for the calendar year. Recalls are characterized by date, recall class, product, reason and pounds recalled. More detailed information can be found in each recall announcement posted on the FSIS website.

Establishment specific sampling results for FSIS' Ready-to-Eat Intensified Verification Testing. Current data is updated quarterly; archive data is updated annually. See the FSIS website for additional information.

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for September 2016.

These ESRI shapefiles show spatial data, points on a map. In addition, shapefiles provide attribute data for each point. Shapefile’s attribute data include spatial information such as latitude and longitude, the address, and obligation amount.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for April 2017.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for August 2018.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for January 2016.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for July 2018.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for June 2016.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for June 2017.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for June 2018.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for March 2016.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for May 2016.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for May 2017.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for May 2018.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for November 2017.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for October 2016.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for October 2017.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for September 2016.

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for September 2017.

This dataset provides loan-level information on when USDA Section 514 and 515 properties are projected to pay off their loans and exit USDA’s Multi-Family Housing program. Includes estimated property exit year, whether the loan is prepay eligible and when, loan amount, original loan term and remaining term days, borrower characteristics, property location and characteristics, and more.

Transaction history of property transfers, consolidations, and sales within the USDA Rural Development Multifamily Direct Loan programs: Section 515 Rural Rental Housing and Section 514 Farm Labor Housing. Includes new property ID numbers and associated old property ID numbers, transaction type indicators, and effective dates. Requires merging with “USDA Rural Development Multifamily Section 515 Rural Rental Housing and Section 514 Farm Labor Housing Property Characteristics” to obtain property address and other characteristics based on new property ID number.

Property locations and characteristics for USDA Rural Development Multifamily Direct Loan programs: Section 515 Rural Rental Housing and Section 514 Farm Labor Housing. Includes latitude and longitude coordinates, property address, type of development, date of operation, profit type, management agent, loan program identifier, Low Income Housing Tax Credit identifier and expiration date, Multifamily Preservation and Revitalization program identifier, total units, USDA Section 521 rental assistance units, units by bedroom size, and vacant units.

Aggregated tenant characteristics for USDA Rural Development Multifamily Direct Loan programs: Section 515 Rural Rental Housing and Section 514 Farm Labor Housing. Includes property address and aggregated demographic information including female headed-households, elderly aged 62 or older, minors, disability status, race, and ethnicity. Also includes average annual income, average annual income by source of income, cost-burden indicator, zero income indicator, and rental assistance subsidy counts by type of assistance. Can be merged with “USDA Rural Development Multifamily Section 515 Rural Rental Housing and Section 514 Farm Labor Housing Property Characteristics” to link to property characteristics, as well as “USDA Rural Development Multifamily Section 515 Rural Rental Housing and Section 514 Farm Labor Housing Properties Transfers, Consolidations, and Sales” to link with property transaction histories.

This data is used to determine eligibility for certain USDA Single Family Housing and Multi-Family Housing loan and grant programs.

This data is used to determine eligibility for certain USDA RBS loan and grant programs.

This data is used to determine eligibility for certain USDA Intermediary Relending Programs.

This data is used to determine eligibility for certain USDA Water and Environmental Programs.

Data provides current information regarding single family homes and ranches for sale by the U.S. Federal Government. These previously owned properties are for sale by public auction or other method depending on the property.

Borrower, property and loan characteristics for all active Section 502 Guaranteed Loans, aggregated by Congressional District. Borrower characteristics include: income, debt-income ratio, race, ethnicity, marital status, dependents, household size, first-time homebuyer status, age and disability status. Property characteristics include: project type (PUD, Condo, Coop), housing structure (detached, attached), manufactured home, living area. Loan characteristics include: loan request amount, loan amount, loan-to-value ratio, and appraised value.Property characteristics include: project type (PUD, Condo, Coop), housing structure (detached, attached), manufactured home, living area. Loan characteristics include: loan request amount, loan amount, loan-to-value ratio, and appraised value.

Borrower, property and loan characteristics for all active Section 502 Guaranteed Loans, aggregated by county. Borrower characteristics include: income, debt-income ratio, race, ethnicity, marital status, dependents, household size, first-time homebuyer status, age and disability status. Property characteristics include: project type (PUD, Condo, Coop), housing structure (detached, attached), manufactured home, living area. Loan characteristics include: loan request amount, loan amount, loan-to-value ratio, and appraised value.

Active loan characteristics in USDA RD Section 538 Multifamily Guaranteed Loan program, including loan, property, and community characteristics. Loan characteristics include obligation fiscal year, lender, borrower, loan closing date, loan amount, total development cost, loan to cost ratio, and federal LIHTC tax credit indicator. Property characteristics include location and address, colonias or tribal location indicator, EZ/EC location indicator, project size, project type, construction type, number of units by bedroom size, and average contract rent by bedroom size. Community characteristics include the area population and median household income at time of obligation.

Active borrower characteristics aggregated at the Congressional District level of geography, including number of borrowers, income levels, race, ethnicity, marital status, number of children in household, and average household size.

Active borrower characteristics aggregated at the county level of geography, including number of borrowers, income levels, race, ethnicity, marital status, number of children in household, and average household size.

Active loan characteristics aggregated at the Congressional District level of geography, including number of loans, average loan amount, average loan amount by 5 year ranges, number of loans to Section 523 Mutual Self Help Housing program participants, and number of leveraged loans.

Active loan characteristics aggregated at the county level of geography, including number of loans, average loan amount, average loan amount by 5 year ranges, number of loans to Section 523 Mutual Self Help Housing program participants, and number of leveraged loans.